1. Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for 4-dimensional (4D) binning seismic data collected with different acquisition geometries.
2. Discussion of the Background
Marine seismic data acquisition and processing generate an image of a geophysical structure (subsurface) under the seafloor. While this image/profile does not provide a precise location for oil and gas reservoirs, it suggests, to those trained in the field, the presence or absence of oil and/or gas reservoirs. Thus, providing a high-resolution image of the subsurface is an ongoing process for the exploration of natural resources, including, among others, oil and/or gas.
During a seismic gathering process, as shown in FIG. 1, a vessel 10 tows an array of seismic receivers 11 located on streamers 12. The streamers may be disposed horizontally, i.e., lying at a constant depth relative to the ocean surface 14, or may have spatial arrangements other than horizontal, e.g., variable-depth arrangement. The vessel 10 also tows a seismic source array 16 configured to generate a seismic wave 18. The seismic wave 18 propagates downward, toward the seafloor 20, and penetrates the seafloor until, eventually, a reflecting structure 22 (reflector) reflects the seismic wave. The reflected seismic wave 24 propagates upward until it is detected by receiver 11 on streamer 12. Based on this data, an image of the subsurface is generated.
Alternatively, ocean bottom cables (OBC) or ocean bottom nodes (OBN) and seismometers (OBS) may be used to record the seismic data. FIG. 2 shows an OBC 30 that includes plural receivers 32 distributed on the ocean bottom 20, which may be connected to each other (or may be independent OBN/OBS) with a cable 33 that may also be connected to a data collection unit 34. Various means (e.g., underwater vehicle) may be used to retrieve the seismic data from the data collection unit 34 and bring it on the vessel 10 for processing.
One or more of the above-noted techniques may be used to monitor a producing reservoir. For these instances, the goal of 4D processing is to determine how and where earth properties change by evaluating differences in co-processed seismic data acquired at different times, usually before (i.e., the baseline survey) and after (i.e., the monitor survey) a period of fluid production from a petroleum reservoir.
Success of 4D processing depends on how well differences in acquisition are compensated for during data processing and imaging. If these differences are accurately compensated, changes in the subsurface that are related to fluid production can be identified by areas of significant difference between baseline and monitor images after migration. Failure of data processing to accurately compensate for acquisition differences leads to creation of 4D noise, which is an appreciable difference of baseline and monitor migrated images not caused by fluid production and, thus, is unwanted.
A sensitive step of 4D processing is the selection of subsets of the base and monitor data that have similar information content and similar wavefield sampling. If this similarity selection is accurately performed, the level of 4D noise in the migrated images is much reduced. This data selection is commonly achieved by 4D-binning, as described in Brain et al., US Patent 20080170468 A1, and Zahibi et al. (2009, “Simultaneous multi-vintage 4D binning,” 71st EAGE Conference and Exhibition, Extended Abstracts), the contents of both documents being incorporated herein by reference. Traditional 4D-binning selects traces from the base and monitor surveys for further processing based on a set of criteria designed to assess their degree of similarity. All prior work on this topic uses similarity criteria evaluated in the data domain (i.e., before migration).
For example, Brain et al. discloses a method for processing at least two sets of seismic data, each dataset comprising several seismic traces (i,j) grouped by bins (B_i, B_j) and by offset classes (O_i, O_j). This method includes the following steps: calculating at least one attribute (a(i,j)) characteristic of a similarity between a first trace (i) of a first dataset and a second trace (j) of a second dataset, and selecting or not the first and second traces (i,j) according to a selection criterion applied to the calculated attribute (a(i,j)).
This method explicitly groups the traces by bin and offset classes to facilitate the 4D-binning process, which aims to decimate the baseline and monitor surveys to a common level of information and wavefield sampling. The method described by Brain et al. and Zahibi et al. is now widely used in the geophysical industry, and assesses similarity of the traces using surface attributes of the baseline and monitor surveys, for example, the geographic position of traces defined by shot and receiver locations, or by mid-point location and/or offset and/or azimuth. Alternative measures are also based on data-domain trace attributes such as cross-correlation. In other words, traditional 4d-binning methods use a data-domain-related attribute (similarity) to group the traces.
The above-discussed 4D-binning processes work well when the baseline and monitor surveys have similar acquisition geometry, for example, a towed-streamer base and a towed-streamer monitor acquired in similar positions but at different times. However, when the base and monitor surveys have different acquisition geometries, for example, a towed-streamer base and sparse OBN monitor, the surface or data-domain trace attributes used to measure similarity in the 4D-binning process are not a good proxy for similarity of the data's information content, and/or of the wavefield sampling in the datasets.
Differences in both information content and wavefield sampling lead to generation of 4D noise. Therefore, it is desirable to address acquisition differences through more accurate methods of data decimation (more accurate methods for 4D-binning).
The problem of decimating two different datasets to a common level of information and wavefield sampling is also addressed in U.S. Pat. No. 8,339,898 (herein '898), the entire content of which is incorporated herein by reference. The 4D-binning method described in '898 decimates the baseline and monitor data by evaluating similarity using a measure based jointly on (i) interpolation to a common and regular surface geometry, and (ii) surface or data-domain trace attributes (as commonly used in 4D-binning). More specifically, '898 discloses a method that includes, inter alia, computing measures associated with regularization of the seismic data, and computing measures associated with 4D-binning, where the 4D-binning includes selecting traces from the seismic data of time-lapse seismic surveys and discarding at least one trace of the seismic data that is based on considering both the regularization measures and the 4D-binning measures.
The use of an interpolation engine to map data to a common and regular data domain (with base and monitor traces occupying the same geographic locations defined by their shot and receiver positions) facilitates the 4D-binning process by providing a further measure of similarity. Interpolating to a common data domain would reduce the differences of wavefield sampling, with differences in information content evaluated by the simultaneous inclusion of surface or data-domain trace attributes in the 4D-binning process.
However, where the baseline and monitor surveys have very different acquisition geometry, such as towed-streamer base and sparse OBN monitor, the interpolation of traces to a common surface data domain does not ensure common levels of wavefield sampling. Furthermore, the evaluation of similarity using surface or data-domain trace attributes cannot accurately measure similarity of information content, since the grouping of traces by surface attributes does not allow the comparison of similar parts of the seismic wavefield.
The problem of matching two datasets with very different acquisition geometries is addressed in Provisional Patent Application 61/752,626 (herein '626), “Wavefield modelling and 4D-binning for time-lapse processing of surveys from different acquisition datums,” the entire disclosure of which is incorporated herein by reference. In '626, matching is addressed by the use of subsurface wavefield modeling. The subsurface modeling described in '626 uses ray-tracing to a target horizon, with or without re-datuming of data to a more convenient geometry, to define the subsurface reflection points and incidence angles that should be matched in 4D-binning. The similarity measure used in '626 is made after grouping traces by their subsurface properties (reflection points and incidence angles). Thus, the method incorporates an estimate of subsurface reflection properties, but the subsurface modeling is limited to reflections on a target horizon. Furthermore, where a choice of trace pairs exists for a single estimated reflection point and incidence angle, the similarity measures used to select traces resorts to surface or data-domain trace attributes (albeit ones applied to traces grouped by subsurface properties).
One weakness of the above-discussed 4D-binning methods is the reliance on grouping together of traces from the baseline and monitor prior to evaluating their similarity. Where the acquisition geometries of baseline and monitor are very similar and have similar positioning, these methods work well. However, the situation is different when the acquisition geometries are significantly different; trace grouping based on surface attributes (such as offset or spatial trace bin) cannot ensure that the right part of the monitor dataset is being compared with the equivalent part of the baseline dataset. Where the subsurface modeling technique is used as described in '626, the trace grouping is more accurate, but it still requires data-domain measures of similarity where a choice of traces exists.
Thus, there is a need for a new 4D-binning method that does not suffer from the limitations noted above.